Autonomous Learning Mode based on a Four-Element Teaching Design for Visual Communication Course

Authors

  • Yan Zhao University of Science and Technology Liaoning
  • Yuemeng Luo University of Science and Technology Liaoning

DOI:

https://doi.org/10.3991/ijet.v15i19.17399

Keywords:

Four-element teaching, autonomous learning, new K-means algorithm, visual communication

Abstract


With the development and improvement of information transfer efficiency in this era, visual communication has people's life and communication. However, the thinking in the traditional visual communication course is fixed, and students' learning enthusiasm is weak. Hence, this study proposes an autonomous learning mode by using a four-element teaching design. First, we propose relevant knowledge and ability, learning task, supporting program, and special practice as four elements related to education program. Second, we integrate them into the autonomous learning model to help students achieve learning transfer. Third, we use the new K-means algorithm to collect students' autonomous learning behavior data in the online course of visual communication and divide the data into three levels (high, medium, and low levels) for analysis. Finally, we design autonomous learning teaching cases for visual communication by using the proposed four-element teaching design and analyze the teaching effect. Our results show that the proposed learning mode enables a long learning video watching time for students, which in turn produces a high percentage of completed tasks. This result verifies that the application of the autonomous learning mode based on four-element teaching design can improve students' autonomous learning ability. Therefore, this mode is worth promoting.

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Published

2020-10-05

How to Cite

Zhao, Y., & Luo, Y. (2020). Autonomous Learning Mode based on a Four-Element Teaching Design for Visual Communication Course. International Journal of Emerging Technologies in Learning (iJET), 15(19), pp. 66–82. https://doi.org/10.3991/ijet.v15i19.17399

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Section

Papers